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# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import TYPE_CHECKING
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from ...utils import _LazyModule
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from ...utils.import_utils import define_import_structure
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if TYPE_CHECKING:
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from .tokenization_phobert import *
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else:
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import sys
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_file = globals()["__file__"]
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sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
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# coding=utf-8
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# Copyright (c) 2020, VinAI Research and the HuggingFace Inc. team.
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# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tokenization classes for PhoBERT"""
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import os
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import re
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from shutil import copyfile
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from typing import Optional
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from ...tokenization_utils import PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.txt",
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"merges_file": "bpe.codes",
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}
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def get_pairs(word):
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"""
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Return set of symbol pairs in a word.
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Word is represented as tuple of symbols (symbols being variable-length strings).
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"""
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pairs = set()
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prev_char = word[0]
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for char in word[1:]:
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pairs.add((prev_char, char))
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prev_char = char
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pairs = set(pairs)
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return pairs
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class PhobertTokenizer(PreTrainedTokenizer):
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"""
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Construct a PhoBERT tokenizer. Based on Byte-Pair-Encoding.
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This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
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this superclass for more information regarding those methods.
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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merges_file (`str`):
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Path to the merges file.
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bos_token (`st`, *optional*, defaults to `"<s>"`):
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the beginning of
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sequence. The token used is the `cls_token`.
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</Tip>
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the end of sequence.
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The token used is the `sep_token`.
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</Tip>
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sep_token (`str`, *optional*, defaults to `"</s>"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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cls_token (`str`, *optional*, defaults to `"<s>"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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mask_token (`str`, *optional*, defaults to `"<mask>"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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def __init__(
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self,
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vocab_file,
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merges_file,
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bos_token="<s>",
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eos_token="</s>",
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sep_token="</s>",
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cls_token="<s>",
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unk_token="<unk>",
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pad_token="<pad>",
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mask_token="<mask>",
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**kwargs,
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):
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self.vocab_file = vocab_file
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self.merges_file = merges_file
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self.encoder = {}
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self.encoder[str(bos_token)] = 0
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self.encoder[str(pad_token)] = 1
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self.encoder[str(eos_token)] = 2
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self.encoder[str(unk_token)] = 3
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self.add_from_file(vocab_file)
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self.decoder = {v: k for k, v in self.encoder.items()}
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with open(merges_file, encoding="utf-8") as merges_handle:
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merges = merges_handle.read().split("\n")[:-1]
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merges = [tuple(merge.split()[:-1]) for merge in merges]
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self.bpe_ranks = dict(zip(merges, range(len(merges))))
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self.cache = {}
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super().__init__(
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bos_token=bos_token,
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eos_token=eos_token,
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unk_token=unk_token,
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sep_token=sep_token,
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cls_token=cls_token,
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pad_token=pad_token,
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mask_token=mask_token,
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**kwargs,
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)
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def build_inputs_with_special_tokens(
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self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
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) -> list[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. A PhoBERT sequence has the following format:
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- single sequence: `<s> X </s>`
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- pair of sequences: `<s> A </s></s> B </s>`
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Args:
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token_ids_0 (`list[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (`list[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`list[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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if token_ids_1 is None:
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return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
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cls = [self.cls_token_id]
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sep = [self.sep_token_id]
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return cls + token_ids_0 + sep + sep + token_ids_1 + sep
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def get_special_tokens_mask(
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self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None, already_has_special_tokens: bool = False
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) -> list[int]:
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"""
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` method.
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Args:
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token_ids_0 (`list[int]`):
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List of IDs.
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token_ids_1 (`list[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`list[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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if token_ids_1 is None:
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return [1] + ([0] * len(token_ids_0)) + [1]
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return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
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def create_token_type_ids_from_sequences(
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self, token_ids_0: list[int], token_ids_1: Optional[list[int]] = None
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) -> list[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. PhoBERT does not
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make use of token type ids, therefore a list of zeros is returned.
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Args:
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token_ids_0 (`list[int]`):
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List of IDs.
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token_ids_1 (`list[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`list[int]`: List of zeros.
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"""
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sep = [self.sep_token_id]
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cls = [self.cls_token_id]
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if token_ids_1 is None:
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return len(cls + token_ids_0 + sep) * [0]
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return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
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@property
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def vocab_size(self):
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return len(self.encoder)
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def get_vocab(self):
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return dict(self.encoder, **self.added_tokens_encoder)
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def bpe(self, token):
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if token in self.cache:
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return self.cache[token]
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word = tuple(token)
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word = tuple(list(word[:-1]) + [word[-1] + "</w>"])
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pairs = get_pairs(word)
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if not pairs:
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return token
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while True:
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bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
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if bigram not in self.bpe_ranks:
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break
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first, second = bigram
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new_word = []
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i = 0
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while i < len(word):
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try:
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j = word.index(first, i)
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except ValueError:
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new_word.extend(word[i:])
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break
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else:
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new_word.extend(word[i:j])
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i = j
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if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
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new_word.append(first + second)
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i += 2
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else:
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new_word.append(word[i])
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i += 1
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new_word = tuple(new_word)
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word = new_word
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if len(word) == 1:
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break
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else:
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pairs = get_pairs(word)
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word = "@@ ".join(word)
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word = word[:-4]
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self.cache[token] = word
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return word
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def _tokenize(self, text):
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"""Tokenize a string."""
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split_tokens = []
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words = re.findall(r"\S+\n?", text)
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for token in words:
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split_tokens.extend(list(self.bpe(token).split(" ")))
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return split_tokens
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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return self.encoder.get(token, self.encoder.get(self.unk_token))
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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return self.decoder.get(index, self.unk_token)
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def convert_tokens_to_string(self, tokens):
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"""Converts a sequence of tokens (string) in a single string."""
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out_string = " ".join(tokens).replace("@@ ", "").strip()
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return out_string
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple[str]:
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if not os.path.isdir(save_directory):
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logger.error(f"Vocabulary path ({save_directory}) should be a directory")
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return
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out_vocab_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
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)
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out_merge_file = os.path.join(
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
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)
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if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
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copyfile(self.vocab_file, out_vocab_file)
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elif not os.path.isfile(self.vocab_file):
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with open(out_vocab_file, "wb") as fi:
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content_spiece_model = self.sp_model.serialized_model_proto()
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fi.write(content_spiece_model)
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if os.path.abspath(self.merges_file) != os.path.abspath(out_merge_file):
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copyfile(self.merges_file, out_merge_file)
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return out_vocab_file, out_merge_file
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# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
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# filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
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# tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
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# tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
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# return ''.join(tokens_generated_so_far)
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def add_from_file(self, f):
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"""
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Loads a pre-existing dictionary from a text file and adds its symbols to this instance.
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"""
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if isinstance(f, str):
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try:
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with open(f, "r", encoding="utf-8") as fd:
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self.add_from_file(fd)
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except FileNotFoundError as fnfe:
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raise fnfe
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except UnicodeError:
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raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset")
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return
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lines = f.readlines()
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for lineTmp in lines:
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line = lineTmp.strip()
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idx = line.rfind(" ")
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if idx == -1:
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raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'")
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word = line[:idx]
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self.encoder[word] = len(self.encoder)
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__all__ = ["PhobertTokenizer"]
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